Postdoctoral Researcher - Optimization with Embedded Machine Learning Surrogates

ExxonMobil is seeking a highly motivated Postdoctoral Researcher specializing in the integration of mathematical optimization and machine learning through surrogate modeling., * Develop optimization frameworks with embedded ML-based surrogate models for complex systems.

• Design and implement formulations that integrate neural networks and other surrogate models into optimization problems (e.g., MIP, MINLP, and nonconvex programs).

• Investigate trade-offs between surrogate model fidelity and optimization tractability.

• Develop specialized solution algorithms for challenging problem structures, including bilinear and nonconvex formulations.

• Explore hybrid solution approaches combining:

• Mathematical programming (e.g., MIP/MINLP)

• Gradient-based optimization (e.g., SLSQP)

• Derivative-free optimization (e.g., NOMAD)

Leverage tools such as GurobiML, OMLT, and decomposition methods

Apply developed methods to high-impact business problems across upstream, downstream, and low-carbon solutions.

Communicate results through technical reports, publications, and presentations.

Example Research & Application Areas

• Optimization with embedded neural network surrogates
• Learning-based surrogate modeling for physics-based systems
• Nonconvex and bilinear optimization arising from ML model integration
• Difference-of-convex (DC) programming and relaxations
• Gradient-based vs. derivative-free optimization strategies
• Hybrid optimization algorithms combining ML and OR

The ideal candidate is a recent Ph.D. graduate with strong expertise in operations research, mixed integer linear or nonlinear optimization, and machine learning, with interest in solving real-world industrial problems involving complex physical systems., * Ph.D. in Operations Research, Industrial Engineering, Applied Mathematics, or a closely related field.

• Strong background in mathematical optimization, including nonlinear and mixed-integer optimization.

• Demonstrated research experience in at least one of the following:

• Optimization with embedded machine learning models

• Surrogate-based optimization

• Nonconvex or bilinear optimization

Knowledge of machine learning models used for surrogate modeling (e.g., neural networks, regression models).

Strong programming skills in Python.

Experience with optimization solvers (e.g., Gurobi, CPLEX, IPOPT).

Strong analytical, problem-solving, and communication skills.

Ability to work in multidisciplinary teams with domain experts.

Preferred Qualifications

• Experience with tools such as GurobiML, OMLT, or similar ML-to-optimization frameworks.
• Experience with derivative-free optimization methods (e.g., NOMAD, Bayesian optimization).
• Knowledge of gradient-based nonlinear optimization methods (e.g., SLSQP).
• Experience working with large-scale industrial or engineering systems.
• Understanding of surrogate model training and validation trade-offs.
• Strong publication record
• Experience developing reusable optimization frameworks or toolkits.

Desired Attributes

• Interest in solving complex, large-scale industrial decision problems.
• Ability to balance model fidelity, scalability, and computational performance.
• Strong collaboration skills with both technical and domain experts.
• Self-driven with the ability to independently lead research initiatives.

Duration

At ExxonMobil, our vision is to lead in energy innovations that advance modern living while reducing emissions. As one of the world's largest publicly traded energy and chemical companies, we are powered by a unique and diverse workforce fueled by the pride in what we do and what we stand for. The success of our Upstream, Product Solutions and Low Carbon Solutions businesses is the result of the talent, curiosity and drive of our people. They bring solutions every day to optimize our strategy in energy, chemicals, lubricants and lower-emissions technologies. We invite you to bring your ideas to ExxonMobil to help create sustainable solutions that improve quality of life and meet society's evolving needs. Learn more about our What and our Why and how we can work together . Why Join ExxonMobil? At ExxonMobil, we apply advanced optimization and machine learning techniques to solve some of the most challenging problems in energy, manufacturing, and low-carbon technologies. In this role, you will work on cutting-edge methods at the intersection of OR and AI, directly impacting critical business decisions and shaping next-generation computational decision-support capabilities., Exxon Mobil Corporation has numerous affiliates, many with names that include ExxonMobil, Exxon, Esso and Mobil. For convenience and simplicity, those terms and terms like corporation, company, our, we and its are sometimes used as abbreviated references to specific affiliates or affiliate groups. Abbreviated references describing global or regional operational organizations and global or regional business lines are also sometimes used for convenience and simplicity. Similarly, ExxonMobil has business relationships with thousands of customers, suppliers, governments, and others. For convenience and simplicity, words like venture, joint venture, partnership, co-venturer, and partner are used to indicate business relationships involving common activities and interests, and those words may not indicate precise legal relationships.